![](\"data/river/ADCP_transect/TRTS_transect_map.png\"){kind=spacer}
"
+ ""
]
},
{
@@ -2396,7 +2401,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.7"
+ "version": "3.12.7"
}
},
"nbformat": 4,
diff --git a/examples/PacWave_resource_characterization_example.ipynb b/examples/PacWave_resource_characterization_example.ipynb
index 3515e1189..f0484365d 100644
--- a/examples/PacWave_resource_characterization_example.ipynb
+++ b/examples/PacWave_resource_characterization_example.ipynb
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -55,7 +55,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -75,12 +75,12 @@
" <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n",
" <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n",
" <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n",
- " <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n",
+ " <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>\n",
" <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n",
" <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n",
" <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n",
" <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css"/>\n",
- " <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>\n",
+ " <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap-glyphicons.css"/>\n",
" <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css"/>\n",
" <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>\n",
" <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>\n",
@@ -88,7 +88,7 @@
" <meta name="viewport" content="width=device-width,\n",
" initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n",
" <style>\n",
- " #map_7a9a886826aa4c3a7c3e4d5ab04aa563 {\n",
+ " #map_e036d6e8c10f3d789429aa1ce6ab8caa {\n",
" position: relative;\n",
" width: 100.0%;\n",
" height: 100.0%;\n",
@@ -102,14 +102,14 @@
"<body>\n",
" \n",
" \n",
- " <div class="folium-map" id="map_7a9a886826aa4c3a7c3e4d5ab04aa563" ></div>\n",
+ " <div class="folium-map" id="map_e036d6e8c10f3d789429aa1ce6ab8caa" ></div>\n",
" \n",
"</body>\n",
"<script>\n",
" \n",
" \n",
- " var map_7a9a886826aa4c3a7c3e4d5ab04aa563 = L.map(\n",
- " "map_7a9a886826aa4c3a7c3e4d5ab04aa563",\n",
+ " var map_e036d6e8c10f3d789429aa1ce6ab8caa = L.map(\n",
+ " "map_e036d6e8c10f3d789429aa1ce6ab8caa",\n",
" {\n",
" center: [44.613600975457715, -123.74317583354498],\n",
" crs: L.CRS.EPSG3857,\n",
@@ -118,40 +118,43 @@
" preferCanvas: false,\n",
" }\n",
" );\n",
- " L.control.scale().addTo(map_7a9a886826aa4c3a7c3e4d5ab04aa563);\n",
+ " L.control.scale().addTo(map_e036d6e8c10f3d789429aa1ce6ab8caa);\n",
"\n",
" \n",
"\n",
" \n",
" \n",
- " var tile_layer_f8adce725eaaaa22ff96e180c7f45be5 = L.tileLayer(\n",
- " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n",
- " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n",
- " ).addTo(map_7a9a886826aa4c3a7c3e4d5ab04aa563);\n",
+ " var tile_layer_b5dfc8256710ce8ae9e8591559fcfbbe = L.tileLayer(\n",
+ " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n",
+ " {"attribution": "Map data \\u00a9 OpenStreetMap contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n",
+ " );\n",
+ " \n",
+ " \n",
+ " tile_layer_b5dfc8256710ce8ae9e8591559fcfbbe.addTo(map_e036d6e8c10f3d789429aa1ce6ab8caa);\n",
" \n",
" \n",
- " var marker_24da36b70bd0cad9fae07cfbb56ac3ee = L.marker(\n",
+ " var marker_de7112f3878753acfb1fecf8d56b2e9d = L.marker(\n",
" [44.669, -124.546],\n",
" {}\n",
- " ).addTo(map_7a9a886826aa4c3a7c3e4d5ab04aa563);\n",
+ " ).addTo(map_e036d6e8c10f3d789429aa1ce6ab8caa);\n",
" \n",
" \n",
- " var popup_83710710487612527869b5149b0463c6 = L.popup({"maxWidth": "100%"});\n",
+ " var popup_b187d42b46b5d23c0400fec1198b2c90 = L.popup({"maxWidth": "100%"});\n",
"\n",
" \n",
" \n",
- " var html_bb170149fe417282e32f838dcd17c041 = $(`<div id="html_bb170149fe417282e32f838dcd17c041" style="width: 100.0%; height: 100.0%;"><i> Water depth: 160 m</i></div>`)[0];\n",
- " popup_83710710487612527869b5149b0463c6.setContent(html_bb170149fe417282e32f838dcd17c041);\n",
+ " var html_a9432228d54f57bc2827195a9b6ad806 = $(`<div id="html_a9432228d54f57bc2827195a9b6ad806" style="width: 100.0%; height: 100.0%;"><i> Water depth: 160 m</i></div>`)[0];\n",
+ " popup_b187d42b46b5d23c0400fec1198b2c90.setContent(html_a9432228d54f57bc2827195a9b6ad806);\n",
" \n",
" \n",
"\n",
- " marker_24da36b70bd0cad9fae07cfbb56ac3ee.bindPopup(popup_83710710487612527869b5149b0463c6)\n",
+ " marker_de7112f3878753acfb1fecf8d56b2e9d.bindPopup(popup_b187d42b46b5d23c0400fec1198b2c90)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
- " marker_24da36b70bd0cad9fae07cfbb56ac3ee.bindTooltip(\n",
+ " marker_de7112f3878753acfb1fecf8d56b2e9d.bindTooltip(\n",
" `<div>\n",
" NDBC 46050\n",
" </div>`,\n",
@@ -159,19 +162,19 @@
" );\n",
" \n",
" \n",
- " var marker_dbddb1336a84d104713e8650e00f51e3 = L.marker(\n",
+ " var marker_06fd0ffe0b1db99925ff095f14329484 = L.marker(\n",
" [44.69, -124.13472222222222],\n",
" {}\n",
- " ).addTo(map_7a9a886826aa4c3a7c3e4d5ab04aa563);\n",
+ " ).addTo(map_e036d6e8c10f3d789429aa1ce6ab8caa);\n",
" \n",
" \n",
- " var icon_a53e20210b2e5c393311613f9a03c05b = L.AwesomeMarkers.icon(\n",
+ " var icon_c93f4cff0a993ecd68ae0bca8b72c710 = L.AwesomeMarkers.icon(\n",
" {"extraClasses": "fa-rotate-0", "icon": "th-large", "iconColor": "white", "markerColor": "green", "prefix": "glyphicon"}\n",
" );\n",
- " marker_dbddb1336a84d104713e8650e00f51e3.setIcon(icon_a53e20210b2e5c393311613f9a03c05b);\n",
+ " marker_06fd0ffe0b1db99925ff095f14329484.setIcon(icon_c93f4cff0a993ecd68ae0bca8b72c710);\n",
" \n",
" \n",
- " marker_dbddb1336a84d104713e8650e00f51e3.bindTooltip(\n",
+ " marker_06fd0ffe0b1db99925ff095f14329484.bindTooltip(\n",
" `<div>\n",
" PACWAVE North\n",
" </div>`,\n",
@@ -179,33 +182,36 @@
" );\n",
" \n",
" \n",
- " var marker_f77526d8dc345db0b39ef332a2ca43da = L.marker(\n",
+ " var marker_c9d914cee5c244e5b7f7e19b0ed682ec = L.marker(\n",
" [44.58444444444444, -124.2125],\n",
" {}\n",
- " ).addTo(map_7a9a886826aa4c3a7c3e4d5ab04aa563);\n",
+ " ).addTo(map_e036d6e8c10f3d789429aa1ce6ab8caa);\n",
" \n",
" \n",
- " var icon_1949bab31dc6ffe47a59f36f434944b1 = L.AwesomeMarkers.icon(\n",
+ " var icon_e1e8815abc89952d3b50ba2d6714a29d = L.AwesomeMarkers.icon(\n",
" {"extraClasses": "fa-rotate-0", "icon": "th", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}\n",
" );\n",
- " marker_f77526d8dc345db0b39ef332a2ca43da.setIcon(icon_1949bab31dc6ffe47a59f36f434944b1);\n",
+ " marker_c9d914cee5c244e5b7f7e19b0ed682ec.setIcon(icon_e1e8815abc89952d3b50ba2d6714a29d);\n",
" \n",
" \n",
- " marker_f77526d8dc345db0b39ef332a2ca43da.bindTooltip(\n",
+ " marker_c9d914cee5c244e5b7f7e19b0ed682ec.bindTooltip(\n",
" `<div>\n",
" PACWAVE South\n",
" </div>`,\n",
" {"sticky": true}\n",
" );\n",
" \n",
+ " \n",
+ " tile_layer_b5dfc8256710ce8ae9e8591559fcfbbe.addTo(map_e036d6e8c10f3d789429aa1ce6ab8caa);\n",
+ " \n",
"</script>\n",
"</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>"
],
"text/plain": [
- "\n",
- "![](\"data/strain/loading_setup.png\")
\n",
- " \n",
- "![](\"data/strain/resulting_loads.png\")
\n",
- "
\n",
+ "\n",
+ "\n",
+ "\n",
"\n",
"The blade root (below) inserts inside the blade and mounts the blade to the hub. This interface contains a thin-walled, cuboid section with circular hole through the center for cable routing.\n",
"\n",
- "\n",
- " \n",
- "\n",
- "\n",
- "![](\"data/strain/blade_hub_interface.png\")
\n",
- "
\n",
+ "\n",
"\n",
"Four, 90 degree strain rosettes (top, bottom, left, right) are mounted to the outer flat surfaces of the thin-walled cuboid. Each strain rosette has a local coordinate system, denoted in the figure below. The global coordinate system corresponds to the blade orientation. The strain gauges are calibrated using the known load and measured strain at the blade root.\n",
"\n",
- "\n",
- "\n",
- "![](\"data/strain/global_cs.png\")
\n",
- "
\n",
+ "\n",
"\n",
"We will start by importing the necessary python packages (`pandas`, `xarray`, `numpy` ``matplotlib.pyplot``). Due to the highly variable nature of experimental configurations, these functions are not included in an MHKiT module. This example can be used as a guide to process strain data from other experimental configurations. Pandas is used to read the CSV file robustly. xarray is used to handle the multidimensional data easily. \n",
"\n",
@@ -732,31 +739,31 @@
"Data variables:\n",
" ea (time, rosette) float64 5MB 3.79e-07 -6.23e-07 ... -1.606e-06\n",
" eb (time, rosette) float64 5MB 2.08e-07 1.16e-06 ... -2.114e-05\n",
- " ec (time, rosette) float64 5MB 2.926e-06 2.7e-07 ... -3.896e-05\n",
- "\n",
- "![](\"data/strain/local_cs_full.png\")
\n",
- "
\n",
+ "\n",
"\n",
"Now we can calculate the axial strains and shear strain for each 90 degree rosette. All rosettes are oriented in this way so a Dataset coordinate corresponding to rosette index is convenient here."
]
@@ -1228,49 +1237,49 @@
" ec (time, rosette) float64 5MB 2.926e-06 2.7e-07 ... -3.896e-05\n",
" axial_strain_x (time, rosette) float64 5MB 3.79e-07 ... -1.606e-06\n",
" axial_strain_y (time, rosette) float64 5MB 2.926e-06 2.7e-07 ... -3.896e-05\n",
- " shear_strain (time, rosette) float64 5MB -1.445e-06 ... -8.57e-07\n",
- "\n",
- "![](\"data/strain/rotation.png\")
\n",
- "
\n",
+ "\n",
"\n",
"In this case, we can transform the bending moment from the global coordinate system to align with the blade's flapwise direction."
]
diff --git a/mhkit/acoustics/__init__.py b/mhkit/acoustics/__init__.py
index f91eec6f9..35cb2b7f0 100644
--- a/mhkit/acoustics/__init__.py
+++ b/mhkit/acoustics/__init__.py
@@ -1,8 +1,18 @@
"""
-The `acoustics` package of the MHKiT (Marine and Hydrokinetic Toolkit) library
-provides tools and functionalities for analyzing and visualizing passive
-acoustic monitoring data deployed in water bodies. This package reads in raw
-wav files and conducts basic acoustics analysis and visualization.
+The passive acoustics module provides a set of functions
+for analyzing and visualizing passive acoustic monitoring
+data deployed in water bodies. This package reads in raw
+*.wav* files and conducts basic acoustics analysis and
+visualization.
+
+To start using the module, import it directly from MHKiT:
+``from mhkit import acoustics``. The analysis functions
+are available directly from the main import, while the
+I/O and graphics submodules are available from
+``acoustics.io`` and ``acoustics.graphics``, respectively.
+The base functions are intended to be used on top of the I/O submodule, and
+include functionality to calibrate data, create spectral densities, sound
+pressure levels, and time or band aggregate spectral data.
"""
from mhkit.acoustics import io, graphics
diff --git a/mhkit/acoustics/analysis.py b/mhkit/acoustics/analysis.py
index 66099b326..bd0e2007d 100644
--- a/mhkit/acoustics/analysis.py
+++ b/mhkit/acoustics/analysis.py
@@ -8,26 +8,32 @@
The following functionality is provided:
1. **Frequency Validation and Warning**:
+
- `_fmax_warning`: Ensures specified maximum frequency does not exceed the Nyquist frequency,
adjusting if necessary to avoid aliasing.
2. **Shallow Water Cutoff Frequency**:
+
- `minimum_frequency`: Calculates the minimum frequency cutoff based on water depth and the
speed of sound in water and seabed materials.
3. **Spectral Density Calculations**:
+
- `sound_pressure_spectral_density`: Computes the mean square sound pressure spectral density
using FFT binning with Hanning windowing and 50% overlap.
4. **Calibration**:
+
- `apply_calibration`: Applies calibration adjustments to the spectral density data using
a sensitivity curve, filling missing values as specified.
5. **Spectral Density Level Calculation**:
+
- `sound_pressure_spectral_density_level`: Converts mean square spectral density values to
sound pressure spectral density levels in dB.
6. **Spectral Density Aggregation**:
+
- `band_aggregate`: Aggregates spectral density data into fractional octave bands using
specified statistical methods (e.g., median, mean).
@@ -35,10 +41,12 @@
similar statistical methods.
7. **Sound Pressure Level Calculation**:
+
- `sound_pressure_level`: Computes the overall sound pressure level within a frequency band
from mean square spectral density.
8. **Frequency-Banded Sound Pressure Level**:
+
- `_band_sound_pressure_level`: Helper function for calculating sound pressure levels
over specified frequency bandwidths.
diff --git a/mhkit/acoustics/graphics.py b/mhkit/acoustics/graphics.py
index cb30f972d..888cec835 100644
--- a/mhkit/acoustics/graphics.py
+++ b/mhkit/acoustics/graphics.py
@@ -4,16 +4,18 @@
levels across time and frequency dimensions.
Each plotting function leverages the flexibility of Matplotlib, allowing for passthrough
-of Matplotlib keyword arguments via **kwargs, making it easy to modify plot aspects such as
+of Matplotlib keyword arguments via ``**kwargs``, making it easy to modify plot aspects such as
color, scale, and label formatting.
Key Functions
-------------
1. **plot_spectrogram**:
+
- Generates a spectrogram plot from sound pressure spectral density level data,
with a logarithmic frequency scale by default for improved readability of acoustic data.
2. **plot_spectra**:
+
- Produces a spectral density plot with a log-transformed x-axis, allowing for clear
visualization of spectral density across frequency bands.
"""
diff --git a/mhkit/acoustics/io.py b/mhkit/acoustics/io.py
index 94acc2048..5e04b82d7 100644
--- a/mhkit/acoustics/io.py
+++ b/mhkit/acoustics/io.py
@@ -13,6 +13,7 @@
------------------
1. **Data Reading**:
+
- `read_hydrophone`: Main function to read a WAV file from a hydrophone and
convert it to either a voltage or pressure time series, depending on the
availability of sensitivity data.
@@ -25,10 +26,12 @@
sound pressure calculation.
2. **Audio Export**:
+
- `export_audio`: Converts processed sound pressure data back into a WAV file
format, with optional gain adjustment to improve playback quality.
3. **Data Extraction**:
+
- `_read_wav_metadata`: Extracts metadata from a WAV file, including bit depth
and other header information.
diff --git a/mhkit/mooring/graphics.py b/mhkit/mooring/graphics.py
index 389953c45..0ba9bd52b 100644
--- a/mhkit/mooring/graphics.py
+++ b/mhkit/mooring/graphics.py
@@ -1,6 +1,4 @@
"""
-graphics.py
-
This module provides a function for creating animated visualizations of a
MoorDyn node position dataset using the matplotlib animation API.
diff --git a/mhkit/power/quality.py b/mhkit/power/quality.py
index a604a8694..3e020f7a6 100644
--- a/mhkit/power/quality.py
+++ b/mhkit/power/quality.py
@@ -1,10 +1,10 @@
"""
This module contains functions for calculating various aspects of power quality,
-particularly focusing on the analysis of harmonics and interharmonics in electrical
-power systems. These functions are designed to assist in power quality assessments
-by providing tools to analyze voltage and current signals for their harmonic
-and interharmonic components based on the guidelines and methodologies
-outlined in IEC 61000-4-7.
+particularly focusing on the analysis of harmonics, interharmonics and distortion
+in electrical power systems. These functions are designed to assist in power
+quality assessments by providing tools to analyze voltage and current signals
+for their harmonic and interharmonic components based on the guidelines and methodologies
+outlined in IEC 61000-4-7:2008 ED2 and in IEC 62600-30:2018 ED1.
Functions in this module include:
diff --git a/mhkit/tests/wave/io/test_swan.py b/mhkit/tests/wave/io/test_swan.py
index 6388bac2b..94a3ba3d0 100644
--- a/mhkit/tests/wave/io/test_swan.py
+++ b/mhkit/tests/wave/io/test_swan.py
@@ -35,7 +35,7 @@ def setUpClass(self):
self.swan_block_txt_file = join(swan_datadir, "SWANOUTBlock.DAT")
self.expected_table = pd.read_csv(
self.table_file,
- sep="\s+",
+ sep="\\s+",
comment="%",
names=["Xp", "Yp", "Hsig", "Dir", "RTpeak", "TDir"],
)
diff --git a/mhkit/tidal/graphics.py b/mhkit/tidal/graphics.py
index 278fdc8a8..151fac479 100644
--- a/mhkit/tidal/graphics.py
+++ b/mhkit/tidal/graphics.py
@@ -39,8 +39,8 @@ def _initialize_polar(ax=None, metadata=None, flood=None, ebb=None):
plt.title(metadata["name"])
# List of strings for metadata box
bouy_str = [
- f'Lat = {float(metadata["lat"]):0.2f}$\degree$',
- f'Lon = {float(metadata["lon"]):0.2f}$\degree$',
+ f'Lat = {float(metadata["lat"]):0.2f}$\\degree$',
+ f'Lon = {float(metadata["lon"]):0.2f}$\\degree$',
]
# Create string for text box
bouy_data = "\n".join(bouy_str)
diff --git a/mhkit/wave/graphics.py b/mhkit/wave/graphics.py
index ba8e536ff..00afefbab 100644
--- a/mhkit/wave/graphics.py
+++ b/mhkit/wave/graphics.py
@@ -64,7 +64,7 @@ def plot_elevation_timeseries(eta, ax=None):
t = eta[time_dimension]
for var in eta.data_vars:
- ax = _xy_plot(t, eta[var], fmt="-", xlabel="Time", ylabel="$\eta$ [m]", ax=ax)
+ ax = _xy_plot(t, eta[var], fmt="-", xlabel="Time", ylabel="$\\eta$ [m]", ax=ax)
return ax
@@ -242,7 +242,7 @@ def plot_chakrabarti(H, lambda_w, D, ax=None):
KC = H / D
Diffraction = np.pi * D / lambda_w
- label = f"$H$ = {H:g}, $\lambda_w$ = {lambda_w:g}, $D$ = {D:g}"
+ label = f"$H$ = {H:g}, $\\lambda_w$ = {lambda_w:g}, $D$ = {D:g}"
ax.plot(Diffraction, KC, "o", label=label)
if (
@@ -268,7 +268,7 @@ def plot_chakrabarti(H, lambda_w, D, ax=None):
ax.text(
1,
7,
- "wave\nbreaking\n$H/\lambda_w > 0.14$",
+ "wave\nbreaking\n$H/\\lambda_w > 0.14$",
ha="center",
va="center",
fontstyle="italic",
diff --git a/mhkit/wave/io/ndbc.py b/mhkit/wave/io/ndbc.py
index 3356358cd..c0fa28683 100644
--- a/mhkit/wave/io/ndbc.py
+++ b/mhkit/wave/io/ndbc.py
@@ -98,7 +98,7 @@ def read_file(file_name, missing_values=["MM", 9999, 999, 99], to_pandas=True):
if header_commented:
data = pd.read_csv(
file_name,
- sep="\s+",
+ sep="\\s+",
header=None,
names=header,
comment="#",
@@ -107,7 +107,7 @@ def read_file(file_name, missing_values=["MM", 9999, 999, 99], to_pandas=True):
# If first line is not commented, then the first row can be used as header
else:
data = pd.read_csv(
- file_name, sep="\s+", header=0, comment="#", parse_dates=[parse_vals]
+ file_name, sep="\\s+", header=0, comment="#", parse_dates=[parse_vals]
)
# Convert index to datetime
@@ -403,13 +403,13 @@ def request_data(parameter, filenames, proxy=None, clear_cache=False, to_pandas=
response = requests.get(file_url, proxies=proxy)
try:
data = zlib.decompress(response.content, 16 + zlib.MAX_WBITS)
- df = pd.read_csv(BytesIO(data), sep="\s+", low_memory=False)
+ df = pd.read_csv(BytesIO(data), sep="\\s+", low_memory=False)
# catch when units are included below the header
firstYear = df["MM"][0]
if isinstance(firstYear, str) and firstYear == "mo":
df = pd.read_csv(
- BytesIO(data), sep="\s+", low_memory=False, skiprows=[1]
+ BytesIO(data), sep="\\s+", low_memory=False, skiprows=[1]
)
except zlib.error:
msg = (
diff --git a/mhkit/wave/io/swan.py b/mhkit/wave/io/swan.py
index c344561d0..b86cf44cc 100644
--- a/mhkit/wave/io/swan.py
+++ b/mhkit/wave/io/swan.py
@@ -41,16 +41,18 @@ def read_table(swan_file, to_pandas=True):
if metaDict["Table"].endswith("SWAN"):
metaDict["Table"] = metaDict["Table"].split(" SWAN")[:-1]
if i == header_line_number:
- header = re.split("\s+", line.rstrip().strip("%").lstrip())
+ header = re.split("\\s+", line.rstrip().strip("%").lstrip())
metaDict["header"] = header
if i == header_line_number + 1:
units = re.split(
- "\s+", line.strip(" %\n").replace("[", "").replace("]", "")
+ "\\s+", line.strip(" %\n").replace("[", "").replace("]", "")
)
metaDict["units"] = units
f.close()
- swan_data = pd.read_csv(swan_file, sep="\s+", comment="%", names=metaDict["header"])
+ swan_data = pd.read_csv(
+ swan_file, sep="\\s+", comment="%", names=metaDict["header"]
+ )
if not to_pandas:
swan_data = convert_to_dataset(swan_data)
@@ -144,7 +146,7 @@ def _read_block_txt(swan_file):
columns_position = None
if not line.startswith("%"):
- raw_data = " ".join(re.split(" |\.", line.strip(" \n"))).split()
+ raw_data = " ".join(re.split(" |\\.", line.strip(" \n"))).split()
index_number = int(raw_data[0])
columns_data = raw_data[1:]
data = []
@@ -230,7 +232,7 @@ def _parse_line_metadata(line):
metaDict = {}
meta = re.sub(
- "\s+", " ", line.replace(",", " ").strip("% \n").replace("**", "vars:")
+ "\\s+", " ", line.replace(",", " ").strip("% \n").replace("**", "vars:")
)
mList = meta.split(":")
elms = [elm.split(" ") for elm in mList]
\n",
- "